As a result of expanding economies and a growing pattern of mass production,mass consumption,and mass disposal,these last decades have seen an increase in the discharge of domestic waste on a global basis.Used plastic...As a result of expanding economies and a growing pattern of mass production,mass consumption,and mass disposal,these last decades have seen an increase in the discharge of domestic waste on a global basis.Used plastic products are frequently pathogen-contaminated,and ought to be handled as hazardous waste.We have learned that companies and policy makers in Vietnam want to transform plastic waste to value and to create its first zero plastic waste cities,but they struggle to make connections,especially across industries,material types and districts.This paper gives a prospective outlook on plastic waste management practices in Vietnam.Based on Japanese plastic waste recycling basic laws systematically,we also discuss and propose the future tasks to apply them in Vietnam.The initiative is expected to help governments,enterprises,and social organizations develop knowledge,capacity,policy planning and plans of action to reduce plastic waste pollution.展开更多
This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,Vietn...This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,Vietnam.Four bands of SPOT 7 image and derivable NDVI,NDWI were used to generate image segments with associated attributes by PCI Geomatics software.These segments were classified into four urban surface types(namely water,impervious surface,vegetation and bare soil)by the proposed model.Alternatively,three training and validation datasets of different sizes were used to verify the robustness of this model.For all tests,the overall accuracies of the classification were approximately 87%,and the Area under Receiver Operating Characteristic curves for each land cover type was 0.97.Also,the performance of this model was examined by comparing several statistical indicators with common benchmark classifiers.The results showed that GMNN out-performed established methods in all comparable indicators.These results suggested that our hybrid model was successfully deployed in the study area and could be used as an alternative classification method for urban land cover studies.In a broader sense,classification methods will be enriched with the active and fast-growing contribution of metaheuristic algorithms.展开更多
文摘As a result of expanding economies and a growing pattern of mass production,mass consumption,and mass disposal,these last decades have seen an increase in the discharge of domestic waste on a global basis.Used plastic products are frequently pathogen-contaminated,and ought to be handled as hazardous waste.We have learned that companies and policy makers in Vietnam want to transform plastic waste to value and to create its first zero plastic waste cities,but they struggle to make connections,especially across industries,material types and districts.This paper gives a prospective outlook on plastic waste management practices in Vietnam.Based on Japanese plastic waste recycling basic laws systematically,we also discuss and propose the future tasks to apply them in Vietnam.The initiative is expected to help governments,enterprises,and social organizations develop knowledge,capacity,policy planning and plans of action to reduce plastic waste pollution.
基金Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant Number[105.99-2016.05].
文摘This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,Vietnam.Four bands of SPOT 7 image and derivable NDVI,NDWI were used to generate image segments with associated attributes by PCI Geomatics software.These segments were classified into four urban surface types(namely water,impervious surface,vegetation and bare soil)by the proposed model.Alternatively,three training and validation datasets of different sizes were used to verify the robustness of this model.For all tests,the overall accuracies of the classification were approximately 87%,and the Area under Receiver Operating Characteristic curves for each land cover type was 0.97.Also,the performance of this model was examined by comparing several statistical indicators with common benchmark classifiers.The results showed that GMNN out-performed established methods in all comparable indicators.These results suggested that our hybrid model was successfully deployed in the study area and could be used as an alternative classification method for urban land cover studies.In a broader sense,classification methods will be enriched with the active and fast-growing contribution of metaheuristic algorithms.
